Principles And Lineage
This document captures the cross-cutting principles that make rFabric a connected robotics platform rather than a set of adjacent products. These principles should shape every page, workflow, API surface, and operational decision.
1. One Entity Graph Across The Lifecycle
Every major object in the platform exists in the Unified Data Model with explicit relationships.
- raw sessions
- processed episodes
- annotations
- curation rulesets
- dataset snapshots
- training runs
- models
- artifacts
- deployments
- updates
- telemetry
- incidents
- maintenance cases
- intervention sessions
The platform does not treat any of these as disconnected side systems.
2. Every Action Is Traceable
Every important lifecycle action should answer:
- who or what triggered it
- on which entity or scope
- through which interface or workflow
- under which policy or approval path
- with what resulting state
Traceability is what makes reproducibility, rollback, incident analysis, and audit-ready operations credible.
3. Automation With Human Gates
The target system is highly automated, but robotics operations still require structured human decision points.
- data review before finalization
- model promotion approval
- rollout sign-off
- maintenance escalation
- teleoperation or supervised autonomy
Human involvement should be encoded into the platform, not handled outside it through ad hoc coordination.
4. Incremental Adoption Is First-Class
Teams should be able to adopt the platform in stages.
- start with ingestion and replay while keeping the current training stack
- adopt curation and dataset finalization next
- centralize release and rollout later
- bring fleet operations into the same control plane over time
This is why the Platform API, integrations, and canonical entity model matter so much: they let partial adoption preserve continuity instead of creating new silos.
5. Coexistence Beats Forced Replacement
Serious robotics teams already use specialist tools. rFabric is designed to become the lifecycle system of record, not to erase every existing interface immediately.
- keep Foxglove or Rerun where they are useful
- keep internal training code where migration is not yet worthwhile
- keep customer-specific operational systems where needed
The platform wins by standardizing lineage, workflows, approvals, and handoffs across those tools.
6. Operations Data Must Feed Development
Field evidence is one of the highest-value data sources in robotics.
- intervention spikes reveal autonomy gaps
- rollout regressions reveal missing evaluation coverage
- maintenance cases reveal hardware or configuration sensitivity
- site-specific anomalies reveal distribution mismatch
That evidence should flow back into review, annotation, curation, evaluation, and training instead of remaining trapped in monitoring dashboards.
7. Regional Compliance Must Be Architectural
Residency, transfer restrictions, retention policy, support-access boundaries, and regulated AI evidence cannot live outside the product if the platform is expected to operate globally. These controls need to be attached to entities, workflows, APIs, rollout policy, and operations access.
8. Robustness Is A Product Feature
State-of-the-art robotics infrastructure is not defined only by advanced ML workflows. It is defined by:
- explicit lifecycle contracts
- reliable provenance
- rollout safety
- operational recoverability
- auditability
- clear separation of concerns
The platform should feel dependable before it feels clever.
End-To-End Lineage Example
One of the platform’s strongest product claims is end-to-end lineage:
robot → raw session → processed episode → annotation and quality score → dataset snapshot → training run → checkpoint → candidate model → approved model → artifact → deployment → telemetry / intervention / maintenance
This chain is what allows teams to answer questions like:
- Which source data produced the model now running in production?
- Which benchmark pack approved that rollout?
- Which field regressions appeared after deployment?
- Which incidents should now become curation and evaluation inputs?
That is the practical meaning of “full lifecycle platform.”